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2. Data science - Wikipedia

   en.wikipedia.org/wiki/Data_science

   Data analysis typically involves working with smaller, structured datasets to answer specific questions or solve specific problems. This can involve tasks such as data cleaning, data visualization, and exploratory data analysis to gain insights into the data and develop hypotheses about relationships between variables. Data analysts typically ...

3. Mu Sigma - Wikipedia

   en.wikipedia.org/wiki/Mu_Sigma

   Mu Sigma is an American data analytics firm providing big data services, decision sciences and helping enterprises in data-driven decision making. The company headquartered in Chicago, Illinois partners with over 140 Fortune 500 companies to enhance their analytical and decision-making capabilities.

4. Data exploration - Wikipedia

   en.wikipedia.org/wiki/Data_exploration

   Data exploration is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems. [1]

5. Data analysis - Wikipedia

   en.wikipedia.org/wiki/Data_analysis

   The data is necessary as inputs to the analysis, which is specified based upon the requirements of those directing the analytics (or customers, who will use the finished product of the analysis). [ 14 ] [ 15 ] The general type of entity upon which the data will be collected is referred to as an experimental unit (e.g., a person or population of ...

6. Chief analytics officer - Wikipedia

   en.wikipedia.org/wiki/Chief_analytics_officer

   Chief analytics officer (CAO) is a job title for the senior manager responsible for the analysis of data within an organization, such as a listed company or an educational institution. The CAO often reports to the chief executive officer .

7. DataOps - Wikipedia

   en.wikipedia.org/wiki/Dataops

   DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics. [1]